Daily dynamics and weekly rhythms: A tutorial on seasonal autoregressive-moving average models combined with day-of-the-week effects.

IF 7.8 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Mohammadhossein Manuel Haqiqatkhah,Ellen L Hamaker
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引用次数: 0

Abstract

Daily diary data of emotional experiences are typically modeled with a first-order autoregressive model to account for possible day-to-day dynamics. However, our emotional experiences are likely influenced by the weekly rhythm of our activities, which may be reflected by (a) day-of-the-week effects (DOWEs), where different weekdays are characterized by different means; and (b) week-to-week dynamics, where weekday-specific activities and experiences have a delayed effect on the emotions that we experience on the same weekday a week later. While DOWEs have been studied occasionally, week-to-week dynamics have been largely ignored in psychological research. We present a set of complementary visualization techniques for detecting weekly rhythms and day-to-day dynamics in time series data. Subsequently, we introduce the family of seasonal autoregressive-moving average models from the econometrics literature, extend them with DOWEs models, and show how their components appear in visualizations. We then provide a tutorial on fitting these models in R, discuss model fit and model selection, and apply them to a daily diary dataset of 56-101 daily measures from 98 individuals. The results suggest that most individuals in the sample may be characterized by patterns and dynamics that the current practices in psychological research cannot capture adequately, and we discuss their implications for current psychological research practices. Reflecting critically on the limitations of our approach, we regard our findings as an initial step to encourage researchers to move beyond the ubiquitous paradigm of lag-1 autoregressive modeling and consider other types of dynamics at different timescales, and put forth ways forward. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
每日动态和每周节奏:季节性自回归移动平均模型与周的影响相结合的教程。
情绪体验的日常日记数据通常用一阶自回归模型建模,以解释可能的日常动态。然而,我们的情绪体验很可能受到我们每周活动节奏的影响,这可能反映在(a)一周中的一天效应(DOWEs),其中不同的工作日有不同的特征;(b)周与周的动态,即工作日特定的活动和经历对一周后我们在同一工作日所经历的情绪有延迟效应。虽然人们偶尔会研究DOWEs,但在心理学研究中,每周的动态变化在很大程度上被忽视了。我们提出了一套互补的可视化技术,用于检测时间序列数据中的每周节律和日常动态。随后,我们从计量经济学文献中引入了季节性自回归移动平均模型家族,用DOWEs模型扩展了它们,并展示了它们的组成部分如何在可视化中出现。然后,我们提供了一个关于在R中拟合这些模型的教程,讨论了模型拟合和模型选择,并将它们应用于来自98个人的56-101个日常测量的日常日记数据集。结果表明,样本中的大多数个体可能具有当前心理学研究实践无法充分捕捉的模式和动态特征,并讨论了它们对当前心理学研究实践的影响。批判性地反思了我们方法的局限性,我们认为我们的发现是鼓励研究人员超越普遍存在的lag-1自回归建模范式,并考虑不同时间尺度下其他类型的动力学的第一步,并提出了未来的方法。(PsycInfo Database Record (c) 2025 APA,版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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